Biology – Quantitative Biology – Neurons and Cognition
Scientific paper
2008-05-19
Biology
Quantitative Biology
Neurons and Cognition
24 pages, 4 figures, 1 supporting information
Scientific paper
In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate vs current (f-I) curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.
Fairhall Adrienne
Hong Sungho
Lundstrom Brian N.
No associations
LandOfFree
Intrinsic gain modulation and adaptive neural coding does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Intrinsic gain modulation and adaptive neural coding, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Intrinsic gain modulation and adaptive neural coding will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-647759